Overview

Dataset statistics

Number of variables35
Number of observations655650
Missing cells5051403
Missing cells (%)22.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory175.1 MiB
Average record size in memory280.0 B

Variable types

CAT20
NUM13
UNSUPPORTED2

Warnings

common_name has a high cardinality: 438 distinct values High cardinality
description has a high cardinality: 654440 distinct values High cardinality
neighborhood has a high cardinality: 130 distinct values High cardinality
direction_code has a high cardinality: 68392 distinct values High cardinality
serial_number has a high cardinality: 654407 distinct values High cardinality
management_unit has a high cardinality: 4596 distinct values High cardinality
greenarea has a high cardinality: 3340 distinct values High cardinality
specie_abbreviation has a high cardinality: 472 distinct values High cardinality
specie_name has a high cardinality: 471 distinct values High cardinality
label has a high cardinality: 654408 distinct values High cardinality
objectid is highly correlated with Unnamed: 0High correlation
Unnamed: 0 is highly correlated with objectidHigh correlation
id_neighborhood is highly correlated with id_districtHigh correlation
id_district is highly correlated with id_neighborhoodHigh correlation
street_type has 655579 (> 99.9%) missing values Missing
streetname has 655579 (> 99.9%) missing values Missing
housenumber has 655594 (> 99.9%) missing values Missing
direction_code has 444722 (67.8%) missing values Missing
zipcode has 655550 (> 99.9%) missing values Missing
id_management_unit has 423135 (64.5%) missing values Missing
management_unit has 423135 (64.5%) missing values Missing
id_greenarea has 235209 (35.9%) missing values Missing
greenarea has 235275 (35.9%) missing values Missing
interference_type has 628745 (95.9%) missing values Missing
crown_diameter has 12472 (1.9%) missing values Missing
height has 11035 (1.7%) missing values Missing
trunk_girth has 8088 (1.2%) missing values Missing
id_management_unit is highly skewed (γ1 = 120.512582) Skewed
trunk_girth is highly skewed (γ1 = 710.7624047) Skewed
description is uniformly distributed Uniform
serial_number is uniformly distributed Uniform
label is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
objectid has unique values Unique
globalid has unique values Unique
housenumber is an unsupported type, check if it needs cleaning or further analysis Unsupported
zipcode is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-01-05 13:29:19.990535
Analysis finished2021-01-05 13:30:51.355151
Duration1 minute and 31.36 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct655650
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327824.5
Minimum0
Maximum655649
Zeros1
Zeros (%)< 0.1%
Memory size5.0 MiB
2021-01-05T14:30:51.735375image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32782.45
Q1163912.25
median327824.5
Q3491736.75
95-th percentile622866.55
Maximum655649
Range655649
Interquartile range (IQR)327824.5

Descriptive statistics

Standard deviation189269.9963
Coefficient of variation (CV)0.5773515901
Kurtosis-1.2
Mean327824.5
Median Absolute Deviation (MAD)163912.5
Skewness1.717475128e-15
Sum2.149381334e+11
Variance3.582313151e+10
MonotocityStrictly increasing
2021-01-05T14:30:51.936515image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
3052141< 0.1%
 
6281201< 0.1%
 
6506471< 0.1%
 
6485981< 0.1%
 
6547411< 0.1%
 
6526921< 0.1%
 
6424511< 0.1%
 
6404021< 0.1%
 
6465451< 0.1%
 
Other values (655640)655640> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
6556491< 0.1%
 
6556481< 0.1%
 
6556471< 0.1%
 
6556461< 0.1%
 
6556451< 0.1%
 

common_name
Categorical

HIGH CARDINALITY

Distinct438
Distinct (%)0.1%
Missing702
Missing (%)0.1%
Memory size5.0 MiB
Plátano de sombra
92721 
Olmo de Siberia
57142 
Acacia del Japón
45079 
Pino piñonero
42000 
Falsa acacia
 
32857
Other values (433)
385149 
ValueCountFrequency (%) 
Plátano de sombra9272114.1%
 
Olmo de Siberia571428.7%
 
Acacia del Japón450796.9%
 
Pino piñonero420006.4%
 
Falsa acacia328575.0%
 
Almez288784.4%
 
Arce negundo269434.1%
 
Aligustre del Japón250653.8%
 
Ciruelo púrpura198403.0%
 
Castaño de indias165082.5%
 
Other values (428)26791540.9%
 
2021-01-05T14:30:52.152737image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique62 ?
Unique (%)< 0.1%
2021-01-05T14:30:52.363624image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length15
Mean length13.85043697
Min length3

objectid
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct655650
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1669576.487
Minimum1341750
Maximum1997555
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:30:52.785494image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1341750
5-th percentile1374533.45
Q11505663.25
median1669576.5
Q31833488.75
95-th percentile1964619.55
Maximum1997555
Range655805
Interquartile range (IQR)327825.5

Descriptive statistics

Standard deviation189270.6633
Coefficient of variation (CV)0.1133644758
Kurtosis-1.199996481
Mean1669576.487
Median Absolute Deviation (MAD)163913
Skewness1.208668388e-06
Sum1.094657824e+12
Variance3.582338397e+10
MonotocityStrictly increasing
2021-01-05T14:30:52.977082image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13433441< 0.1%
 
19930621< 0.1%
 
15519671< 0.1%
 
15499181< 0.1%
 
15560611< 0.1%
 
15540121< 0.1%
 
15437711< 0.1%
 
15417221< 0.1%
 
15478651< 0.1%
 
15458161< 0.1%
 
Other values (655640)655640> 99.9%
 
ValueCountFrequency (%) 
13417501< 0.1%
 
13417511< 0.1%
 
13417521< 0.1%
 
13417531< 0.1%
 
13417541< 0.1%
 
ValueCountFrequency (%) 
19975551< 0.1%
 
19975521< 0.1%
 
19975511< 0.1%
 
19975501< 0.1%
 
19975451< 0.1%
 

description
Categorical

HIGH CARDINALITY
UNIFORM

Distinct654440
Distinct (%)99.8%
Missing12
Missing (%)< 0.1%
Memory size5.0 MiB
Árbol
 
28
ccc arbol
 
17
Arbol ARB_03-10_0000029731
 
14
Arbol ARB_02-05_0000000481
 
13
Palmera
 
9
Other values (654435)
655557 
ValueCountFrequency (%) 
Árbol28< 0.1%
 
ccc arbol17< 0.1%
 
Arbol ARB_03-10_000002973114< 0.1%
 
Arbol ARB_02-05_000000048113< 0.1%
 
Palmera9< 0.1%
 
Arbol ARB_02-04_00000107875< 0.1%
 
�rbol4< 0.1%
 
Arbol4< 0.1%
 
descripcion de palmacea4< 0.1%
 
descripcion del arbol4< 0.1%
 
Other values (654430)655536> 99.9%
 
(Missing)12< 0.1%
 
2021-01-05T14:30:55.696501image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique653326 ?
Unique (%)99.6%
2021-01-05T14:30:55.851760image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length51
Median length26
Mean length26.00959201
Min length2

id_lot
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.884860825
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:30:55.976909image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.485944333
Coefficient of variation (CV)0.3824961561
Kurtosis-0.924032141
Mean3.884860825
Median Absolute Deviation (MAD)1
Skewness-0.2060212094
Sum2547109
Variance2.20803056
MonotocityNot monotonic
2021-01-05T14:30:56.108577image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
414823422.6%
 
513163420.1%
 
313041919.9%
 
611414917.4%
 
28863813.5%
 
1425766.5%
 
ValueCountFrequency (%) 
1425766.5%
 
28863813.5%
 
313041919.9%
 
414823422.6%
 
513163420.1%
 
ValueCountFrequency (%) 
611414917.4%
 
513163420.1%
 
414823422.6%
 
313041919.9%
 
28863813.5%
 

lot
Categorical

Distinct8
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size5.0 MiB
NORESTE
148233 
SURESTE
131634 
OESTE
130414 
SUR
114149 
CENTRO-ESTE
88638 
Other values (3)
42581 
ValueCountFrequency (%) 
NORESTE14823322.6%
 
SURESTE13163420.1%
 
OESTE13041419.9%
 
SUR11414917.4%
 
CENTRO-ESTE8863813.5%
 
CENTRO-OESTE425746.5%
 
PARQUES FORESTALES5< 0.1%
 
PARQUES HISTÓRICOS2< 0.1%
 
(Missing)1< 0.1%
 
2021-01-05T14:30:56.250852image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T14:30:56.362436image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:56.513572image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length7
Mean length6.77132769
Min length3

id_district
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.99168304
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:30:56.685890image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median12
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.359185486
Coefficient of variation (CV)0.446908534
Kurtosis-0.908591753
Mean11.99168304
Median Absolute Deviation (MAD)4
Skewness-0.2268257391
Sum7862323
Variance28.72086908
MonotocityNot monotonic
2021-01-05T14:30:56.861205image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
8570128.7%
 
16525138.0%
 
13467847.1%
 
11425206.5%
 
17411406.3%
 
20401726.1%
 
10396196.0%
 
15393216.0%
 
18367705.6%
 
9337685.2%
 
Other values (11)22602934.5%
 
ValueCountFrequency (%) 
1116481.8%
 
2241693.7%
 
3152692.3%
 
4223293.4%
 
5270034.1%
 
ValueCountFrequency (%) 
21162242.5%
 
20401726.1%
 
19230673.5%
 
18367705.6%
 
17411406.3%
 

district
Categorical

Distinct21
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size5.0 MiB
FUENCARRAL-EL PARDO
57012 
HORTALEZA
52513 
PUENTE DE VALLECAS
46784 
CARABANCHEL
42520 
VILLAVERDE
 
41140
Other values (16)
415679 
ValueCountFrequency (%) 
FUENCARRAL-EL PARDO570128.7%
 
HORTALEZA525138.0%
 
PUENTE DE VALLECAS467847.1%
 
CARABANCHEL425206.5%
 
VILLAVERDE411406.3%
 
SAN BLAS-CANILLEJAS401726.1%
 
LATINA396196.0%
 
CIUDAD LINEAL393216.0%
 
VILLA DE VALLECAS367705.6%
 
MONCLOA-ARAVACA337685.2%
 
Other values (11)22602934.5%
 
2021-01-05T14:30:57.049372image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T14:30:57.235577image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length10
Mean length11.70815832
Min length3

id_neighborhood
Real number (ℝ≥0)

HIGH CORRELATION

Distinct130
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean123.7604309
Minimum11
Maximum215
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:30:57.424049image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile26
Q186
median127
Q3166
95-th percentile205
Maximum215
Range204
Interquartile range (IQR)80

Descriptive statistics

Standard deviation53.45380678
Coefficient of variation (CV)0.4319135477
Kurtosis-0.8724811856
Mean123.7604309
Median Absolute Deviation (MAD)40
Skewness-0.2449853669
Sum81143279
Variance2857.309459
MonotocityNot monotonic
2021-01-05T14:30:57.616207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
183240103.7%
 
166215553.3%
 
86164192.5%
 
171139232.1%
 
116130892.0%
 
131126141.9%
 
191122151.9%
 
134119551.8%
 
117117331.8%
 
152116531.8%
 
Other values (120)50648277.2%
 
ValueCountFrequency (%) 
1137370.6%
 
1221220.3%
 
1313250.2%
 
1418210.3%
 
1519540.3%
 
ValueCountFrequency (%) 
21533480.5%
 
21477371.2%
 
2138930.1%
 
2128380.1%
 
21134080.5%
 

neighborhood
Categorical

HIGH CARDINALITY

Distinct130
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size5.0 MiB
ENSANCHE DE VALLECAS
 
24010
VALDEFUENTES
 
21555
VALVERDE
 
16419
VILLAVERDE ALTO C.H.
 
13923
BUENAVISTA
 
13089
Other values (125)
566652 
ValueCountFrequency (%) 
ENSANCHE DE VALLECAS240103.7%
 
VALDEFUENTES215553.3%
 
VALVERDE164192.5%
 
VILLAVERDE ALTO C.H.139232.1%
 
BUENAVISTA130892.0%
 
ENTREVÍAS126141.9%
 
CASCO HISTÓRICO DE VICÁLVARO122151.9%
 
PALOMERAS SURESTE119551.8%
 
ABRANTES117331.8%
 
PUEBLO NUEVO116531.8%
 
Other values (120)50648277.2%
 
2021-01-05T14:30:57.855310image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T14:30:58.063808image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length10
Mean length10.926354
Min length3
Distinct6
Distinct (%)< 0.1%
Missing19
Missing (%)< 0.1%
Memory size5.0 MiB
JARDINERIA \ ARBOLES \ ARBOL
417777 
JARDINERIA \ ARBOLES \ ARBOL_VIARIO
235089 
JARDINERIA \ PALMACEAS
 
2760
JARDINERIA \ ARBOLES
 
3
JARDINERIA \ TERRIZOS
 
1
ValueCountFrequency (%) 
JARDINERIA \ ARBOLES \ ARBOL41777763.7%
 
JARDINERIA \ ARBOLES \ ARBOL_VIARIO23508935.9%
 
JARDINERIA \ PALMACEAS27600.4%
 
JARDINERIA \ ARBOLES3< 0.1%
 
JARDINERIA \ TERRIZOS1< 0.1%
 
OBRA_CIVIL \ ALCORQUES1< 0.1%
 
(Missing)19< 0.1%
 
2021-01-05T14:30:58.263654image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-01-05T14:30:58.414741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:58.551973image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length28
Mean length30.48387249
Min length3

street_type
Categorical

MISSING

Distinct7
Distinct (%)9.9%
Missing655579
Missing (%)> 99.9%
Memory size5.0 MiB
CALLE
34 
CUESTA
13 
PLAZA
10 
tipoVia
AVENIDA
Other values (2)
 
2
ValueCountFrequency (%) 
CALLE34< 0.1%
 
CUESTA13< 0.1%
 
PLAZA10< 0.1%
 
tipoVia8< 0.1%
 
AVENIDA4< 0.1%
 
AV1< 0.1%
 
Av1< 0.1%
 
(Missing)655579> 99.9%
 
2021-01-05T14:30:58.894816image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)2.8%
2021-01-05T14:30:59.048390image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:59.191506image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.00026386
Min length2

streetname
Categorical

MISSING

Distinct29
Distinct (%)40.8%
Missing655579
Missing (%)> 99.9%
Memory size5.0 MiB
CUESTA DE LA VEGA
13 
Paseo de la Castellana
Murillo
nombreVia
ARROYO DEL OLIVAR
Other values (24)
29 
ValueCountFrequency (%) 
CUESTA DE LA VEGA13< 0.1%
 
Paseo de la Castellana9< 0.1%
 
Murillo9< 0.1%
 
nombreVia8< 0.1%
 
ARROYO DEL OLIVAR3< 0.1%
 
Puerta del Angel Caido3< 0.1%
 
CALLE SEGOVIA2< 0.1%
 
Calle Rosaleda2< 0.1%
 
HACIENDA DE PAVONES2< 0.1%
 
SANTUARIO DE VALVERDE1< 0.1%
 
Other values (19)19< 0.1%
 
(Missing)655579> 99.9%
 
2021-01-05T14:30:59.421629image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20 ?
Unique (%)28.2%
2021-01-05T14:30:59.617791image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length3
Mean length3.001302524
Min length3

housenumber
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing655594
Missing (%)> 99.9%
Memory size5.0 MiB

direction_code
Categorical

HIGH CARDINALITY
MISSING

Distinct68392
Distinct (%)32.4%
Missing444722
Missing (%)67.8%
Memory size5.0 MiB
CALLE SERRANILLOS DEL VALLE 6
 
70
CALLE EMBAJADORES 322
 
57
AVENIDA MAYORAZGO 8
 
56
CALLE ARROYO FONTARRON 483
 
53
CALLE DE LA BAHIA DE MALAGA, 18
 
53
Other values (68387)
210639 
ValueCountFrequency (%) 
CALLE SERRANILLOS DEL VALLE 670< 0.1%
 
CALLE EMBAJADORES 32257< 0.1%
 
AVENIDA MAYORAZGO 856< 0.1%
 
CALLE ARROYO FONTARRON 48353< 0.1%
 
CALLE DE LA BAHIA DE MALAGA, 1853< 0.1%
 
CALLE RAYO VALLECANO DE MADRID 453< 0.1%
 
CALLE PINILLAS 4853< 0.1%
 
CALLE VALCARLOS 650< 0.1%
 
BULEVAR JOSE PRAT 4249< 0.1%
 
RONDA SUR 22948< 0.1%
 
Other values (68382)21038632.1%
 
(Missing)44472267.8%
 
2021-01-05T14:31:00.016996image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique23339 ?
Unique (%)11.1%
2021-01-05T14:31:00.251080image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length60
Median length3
Mean length9.922306108
Min length3

zipcode
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing655550
Missing (%)> 99.9%
Memory size5.0 MiB

serial_number
Categorical

HIGH CARDINALITY
UNIFORM

Distinct654407
Distinct (%)99.8%
Missing67
Missing (%)< 0.1%
Memory size5.0 MiB
ARB_03-10_0000029731
 
14
ARB_02-05_0000000481
 
12
3234245
 
8
PAL_04-21_0000000001
 
8
ARB_04-21_0000003686
 
7
Other values (654402)
655534 
ValueCountFrequency (%) 
ARB_03-10_000002973114< 0.1%
 
ARB_02-05_000000048112< 0.1%
 
32342458< 0.1%
 
PAL_04-21_00000000018< 0.1%
 
ARB_04-21_00000036867< 0.1%
 
ARB_04-21_00000031617< 0.1%
 
ALAMEDA DE OSUNA (BARAJAS)-876< 0.1%
 
ALAMEDA DE OSUNA (BARAJAS)-866< 0.1%
 
ARB_01-01_00000146694< 0.1%
 
ARB_02-04_00000107874< 0.1%
 
Other values (654397)655507> 99.9%
 
(Missing)67< 0.1%
 
2021-01-05T14:31:03.599357image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique653289 ?
Unique (%)99.7%
2021-01-05T14:31:03.805397image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length20
Mean length19.99786014
Min length2

id_management_unit
Real number (ℝ≥0)

MISSING
SKEWED

Distinct4597
Distinct (%)2.0%
Missing423135
Missing (%)64.5%
Infinite0
Infinite (%)0.0%
Mean911877.4847
Minimum3
Maximum9200001973
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:31:04.000608image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile666.6
Q13174
median6423
Q399219
95-th percentile4000421
Maximum9200001973
Range9200001970
Interquartile range (IQR)96045

Descriptive statistics

Standard deviation76317759.97
Coefficient of variation (CV)83.69299742
Kurtosis14523.43721
Mean911877.4847
Median Absolute Deviation (MAD)4805
Skewness120.512582
Sum2.120251934e+11
Variance5.824400488e+15
MonotocityNot monotonic
2021-01-05T14:31:04.201501image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
72978720.1%
 
995538240.1%
 
40004338220.1%
 
40004497630.1%
 
994727300.1%
 
49387150.1%
 
3034346590.1%
 
50196470.1%
 
3070676430.1%
 
995516410.1%
 
Other values (4587)22519934.3%
 
(Missing)42313564.5%
 
ValueCountFrequency (%) 
329< 0.1%
 
457< 0.1%
 
538< 0.1%
 
714< 0.1%
 
927< 0.1%
 
ValueCountFrequency (%) 
920000197316< 0.1%
 
400095424< 0.1%
 
400095315< 0.1%
 
400095237< 0.1%
 
400094921< 0.1%
 

management_unit
Categorical

HIGH CARDINALITY
MISSING

Distinct4596
Distinct (%)2.0%
Missing423135
Missing (%)64.5%
Memory size5.0 MiB
13.Sur, Ronda
 
872
16. María de las Mercedes de Borbón, Calle
 
824
18.Cerro Milano, Avenida
 
822
18.Ensanche de Vallecas, Avenida
 
763
18.Mayorazgo, Avenida
 
730
Other values (4591)
228504 
ValueCountFrequency (%) 
13.Sur, Ronda8720.1%
 
16. María de las Mercedes de Borbón, Calle8240.1%
 
18.Cerro Milano, Avenida8220.1%
 
18.Ensanche de Vallecas, Avenida7630.1%
 
18.Mayorazgo, Avenida7300.1%
 
13.Miguel Hernández, Avenida7150.1%
 
14.Hacienda de Pavones (Moratalaz), Calle6590.1%
 
8.Monforte de Lemos, Avenida6470.1%
 
5.Serrano (Chamartín), Calle6430.1%
 
16.Francisco Javier Saenz de Oiza, Avenida6410.1%
 
Other values (4586)22519934.3%
 
(Missing)42313564.5%
 
2021-01-05T14:31:04.447746image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique218 ?
Unique (%)0.1%
2021-01-05T14:31:04.669170image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length3
Mean length10.60364219
Min length3

id_greenarea
Real number (ℝ≥0)

MISSING

Distinct3362
Distinct (%)0.8%
Missing235209
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean118619.602
Minimum116501
Maximum120458
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:31:04.881819image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum116501
5-th percentile116678
Q1117727
median118683
Q3119670
95-th percentile120295
Maximum120458
Range3957
Interquartile range (IQR)1943

Descriptive statistics

Standard deviation1162.780398
Coefficient of variation (CV)0.009802599044
Kurtosis-1.197063453
Mean118619.602
Median Absolute Deviation (MAD)987
Skewness-0.1717033817
Sum4.98725441e+10
Variance1352058.253
MonotocityNot monotonic
2021-01-05T14:31:05.124995image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11984673471.1%
 
11967073211.1%
 
11905671361.1%
 
11899256500.9%
 
11835444970.7%
 
12024639300.6%
 
11913634910.5%
 
11967133940.5%
 
11997725660.4%
 
11806225470.4%
 
Other values (3352)37256256.8%
 
(Missing)23520935.9%
 
ValueCountFrequency (%) 
11650121< 0.1%
 
11650263< 0.1%
 
11650311< 0.1%
 
1165045< 0.1%
 
11650510< 0.1%
 
ValueCountFrequency (%) 
120458263< 0.1%
 
120456206< 0.1%
 
120455241< 0.1%
 
120454244< 0.1%
 
12045318< 0.1%
 

greenarea
Categorical

HIGH CARDINALITY
MISSING

Distinct3340
Distinct (%)0.8%
Missing235275
Missing (%)35.9%
Memory size5.0 MiB
PARQUE DE PRADOLONGO
 
7347
PARQUE EMPERATRIZ MARIA DE AUSTRIA
 
7321
CUÑA VERDE LA LATINA
 
7136
PARQUE ENRIQUE TIERNO GALVAN
 
5650
PARQUE LINEAL DE PALOMERAS
 
4497
Other values (3335)
388424 
ValueCountFrequency (%) 
PARQUE DE PRADOLONGO73471.1%
 
PARQUE EMPERATRIZ MARIA DE AUSTRIA73211.1%
 
CUÑA VERDE LA LATINA71361.1%
 
PARQUE ENRIQUE TIERNO GALVAN56500.9%
 
PARQUE LINEAL DE PALOMERAS44970.7%
 
PARQUE DE PLATA Y CASTAÑAR39300.6%
 
PARQUE DE LAS CRUCES34910.5%
 
PARQUE SAN ISIDRO33940.5%
 
AMPLIACION PARQUE ARROYO POZUELO25660.4%
 
COL. MARQUES DE SUANCES25470.4%
 
Other values (3330)37249656.8%
 
(Missing)23527535.9%
 
2021-01-05T14:31:05.475540image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique73 ?
Unique (%)< 0.1%
2021-01-05T14:31:05.693177image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length73
Median length17
Mean length15.76133455
Min length3

specie_abbreviation
Categorical

HIGH CARDINALITY

Distinct472
Distinct (%)0.1%
Missing21
Missing (%)< 0.1%
Memory size5.0 MiB
PDA
92721 
UPU
57069 
SJA
45079 
PPI
42000 
RPS
 
32857
Other values (467)
385903 
ValueCountFrequency (%) 
PDA9272114.1%
 
UPU570698.7%
 
SJA450796.9%
 
PPI420006.4%
 
RPS328575.0%
 
CAU287184.4%
 
ANE269434.1%
 
LJA250653.8%
 
PCP198403.0%
 
AHI165082.5%
 
Other values (462)26882941.0%
 
2021-01-05T14:31:05.913492image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique72 ?
Unique (%)< 0.1%
2021-01-05T14:31:06.116187image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length3.036376115
Min length3

specie_name
Categorical

HIGH CARDINALITY

Distinct471
Distinct (%)0.1%
Missing701
Missing (%)0.1%
Memory size5.0 MiB
Platanus x hybrida
92721 
Ulmus pumila
57069 
Sophora japonica
45079 
Pinus pinea
42000 
Robinia pseudoacacia
 
32857
Other values (466)
385223 
ValueCountFrequency (%) 
Platanus x hybrida9272114.1%
 
Ulmus pumila570698.7%
 
Sophora japonica450796.9%
 
Pinus pinea420006.4%
 
Robinia pseudoacacia328575.0%
 
Celtis australis287184.4%
 
Acer negundo269434.1%
 
Ligustrum japonicum250653.8%
 
Prunus cerasifera subsp. Pissartii198403.0%
 
Aesculus hippocastanum165082.5%
 
Other values (461)26814940.9%
 
2021-01-05T14:31:06.319561image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique73 ?
Unique (%)< 0.1%
2021-01-05T14:31:06.521317image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length16
Mean length16.94086327
Min length3

senescence
Categorical

Distinct2
Distinct (%)< 0.1%
Missing4975
Missing (%)0.8%
Memory size5.0 MiB
CADUCIFOLIO
496348 
PERENNIFOLIO
154327 
ValueCountFrequency (%) 
CADUCIFOLIO49634875.7%
 
PERENNIFOLIO15432723.5%
 
(Missing)49750.8%
 
2021-01-05T14:31:06.766747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T14:31:06.963571image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:31:07.286473image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length11
Mean length11.17467704
Min length3
Distinct7
Distinct (%)< 0.1%
Missing647
Missing (%)0.1%
Memory size5.0 MiB
M
357726 
J
228645 
NC
 
28230
T
 
15937
V
 
15845
Other values (2)
 
8620
ValueCountFrequency (%) 
M35772654.6%
 
J22864534.9%
 
NC282304.3%
 
T159372.4%
 
V158452.4%
 
D51600.8%
 
RC34600.5%
 
(Missing)6470.1%
 
2021-01-05T14:31:07.574326image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T14:31:07.725056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:31:07.980418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.050307329
Min length1

interference_type
Categorical

MISSING

Distinct9
Distinct (%)< 0.1%
Missing628745
Missing (%)95.9%
Memory size5.0 MiB
EDIFICIOS
10780 
TRANSITO_PEATONAL
4594 
NO_ALTA_TENSION
4174 
FAROLAS
4105 
OTROS
1556 
Other values (4)
1696 
ValueCountFrequency (%) 
EDIFICIOS107801.6%
 
TRANSITO_PEATONAL45940.7%
 
NO_ALTA_TENSION41740.6%
 
FAROLAS41050.6%
 
OTROS15560.2%
 
INVASION_CALZADA7120.1%
 
SENALES5170.1%
 
VISIBILIDAD_TRAFICO3480.1%
 
ALTA_TENSION119< 0.1%
 
(Missing)62874595.9%
 
2021-01-05T14:31:08.342113image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T14:31:08.536296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:31:08.866486image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length3.330327156
Min length3

status
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
OPERATIVO
655464 
NO PREPARADO
 
166
DESAPARECIDO
 
9
INACTIVO
 
5
ACTIVO
 
4
ValueCountFrequency (%) 
OPERATIVO655464> 99.9%
 
NO PREPARADO166< 0.1%
 
DESAPARECIDO9< 0.1%
 
INACTIVO5< 0.1%
 
ACTIVO4< 0.1%
 
NO_PREPARADO2< 0.1%
 
2021-01-05T14:31:09.249701image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T14:31:09.512458image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:31:09.846957image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length9
Mean length9.000783955
Min length6

label
Categorical

HIGH CARDINALITY
UNIFORM

Distinct654408
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
Árbol:
 
67
Árbol: ARB_03-10_0000029731
 
14
Árbol: ARB_02-05_0000000481
 
12
Árbol: PAL_04-21_0000000001
 
8
Árbol: 3234245
 
8
Other values (654403)
655541 
ValueCountFrequency (%) 
Árbol: 67< 0.1%
 
Árbol: ARB_03-10_000002973114< 0.1%
 
Árbol: ARB_02-05_000000048112< 0.1%
 
Árbol: PAL_04-21_00000000018< 0.1%
 
Árbol: 32342458< 0.1%
 
Árbol: ARB_04-21_00000031617< 0.1%
 
Árbol: ARB_04-21_00000036867< 0.1%
 
Árbol: ALAMEDA DE OSUNA (BARAJAS)-876< 0.1%
 
Árbol: ALAMEDA DE OSUNA (BARAJAS)-866< 0.1%
 
Árbol: ARB_01-01_00000146694< 0.1%
 
Other values (654398)655511> 99.9%
 
2021-01-05T14:31:13.480165image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique653289 ?
Unique (%)99.6%
2021-01-05T14:31:13.806110image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length27
Mean length26.99755357
Min length7

globalid
Categorical

UNIQUE

Distinct655650
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
{62019C6B-D31A-4049-9BAC-8B7A395CE557}
 
1
{83682A1B-088D-4C04-9CA8-E8E48BE3CC86}
 
1
{EDBCB49D-1923-4385-9AA7-393DD2820D05}
 
1
{47E8709B-A777-4902-BA23-DEDDA4DC4B67}
 
1
{D612159F-9E93-467C-950E-8716E3828280}
 
1
Other values (655645)
655645 
ValueCountFrequency (%) 
{62019C6B-D31A-4049-9BAC-8B7A395CE557}1< 0.1%
 
{83682A1B-088D-4C04-9CA8-E8E48BE3CC86}1< 0.1%
 
{EDBCB49D-1923-4385-9AA7-393DD2820D05}1< 0.1%
 
{47E8709B-A777-4902-BA23-DEDDA4DC4B67}1< 0.1%
 
{D612159F-9E93-467C-950E-8716E3828280}1< 0.1%
 
{92AFEB20-8A17-41D3-9CE1-9490E4884E14}1< 0.1%
 
{F845E171-D8B2-47D9-AF5A-A3E073B99EEA}1< 0.1%
 
{4E382CEA-6305-474B-A850-F3CFE2AE1B14}1< 0.1%
 
{FC844079-EFBB-40A2-8752-31A344F8B916}1< 0.1%
 
{59525193-524F-4F1A-9BBB-3189AE52523D}1< 0.1%
 
Other values (655640)655640> 99.9%
 
2021-01-05T14:31:16.980403image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique655650 ?
Unique (%)100.0%
2021-01-05T14:31:17.167924image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length38
Mean length38
Min length38

parent
Real number (ℝ≥0)

Distinct655479
Distinct (%)> 99.9%
Missing132
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4333269.185
Minimum1550265
Maximum4736879
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:31:17.651779image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1550265
5-th percentile4033205.85
Q14166343.25
median4333397.5
Q34499922.75
95-th percentile4634058.15
Maximum4736879
Range3186614
Interquartile range (IQR)333579.5

Descriptive statistics

Standard deviation193581.5932
Coefficient of variation (CV)0.04467333667
Kurtosis0.5511935838
Mean4333269.185
Median Absolute Deviation (MAD)166794.5
Skewness-0.1220132158
Sum2.840535949e+12
Variance3.747383322e+10
MonotocityNot monotonic
2021-01-05T14:31:17.863614image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
155026527< 0.1%
 
35712996< 0.1%
 
35713186< 0.1%
 
47368793< 0.1%
 
35713172< 0.1%
 
43319481< 0.1%
 
43002171< 0.1%
 
44749591< 0.1%
 
40085971< 0.1%
 
43790231< 0.1%
 
Other values (655469)655469> 99.9%
 
(Missing)132< 0.1%
 
ValueCountFrequency (%) 
155026527< 0.1%
 
23141261< 0.1%
 
35712996< 0.1%
 
35713172< 0.1%
 
35713186< 0.1%
 
ValueCountFrequency (%) 
47368793< 0.1%
 
46676751< 0.1%
 
46676741< 0.1%
 
46676731< 0.1%
 
46676721< 0.1%
 

crown_diameter
Real number (ℝ≥0)

MISSING

Distinct250
Distinct (%)< 0.1%
Missing12472
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean4.582883012
Minimum0
Maximum300
Zeros1174
Zeros (%)0.2%
Memory size5.0 MiB
2021-01-05T14:31:18.178554image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum300
Range300
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.16200387
Coefficient of variation (CV)0.6899595434
Kurtosis948.4969667
Mean4.582883012
Median Absolute Deviation (MAD)2
Skewness13.53498469
Sum2947609.53
Variance9.998268476
MonotocityNot monotonic
2021-01-05T14:31:18.472389image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
28302912.7%
 
48063612.3%
 
66618910.1%
 
3622119.5%
 
1505847.7%
 
5505287.7%
 
8418716.4%
 
7332425.1%
 
1.5247413.8%
 
2.5216313.3%
 
Other values (240)12851619.6%
 
ValueCountFrequency (%) 
011740.2%
 
0.12< 0.1%
 
0.121< 0.1%
 
0.161< 0.1%
 
0.271< 0.1%
 
ValueCountFrequency (%) 
3001< 0.1%
 
25012< 0.1%
 
2221< 0.1%
 
2161< 0.1%
 
2061< 0.1%
 

height
Real number (ℝ≥0)

MISSING

Distinct328
Distinct (%)0.1%
Missing11035
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean7.645133235
Minimum0
Maximum180
Zeros2692
Zeros (%)0.4%
Memory size5.0 MiB
2021-01-05T14:31:18.732242image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14.7
median6.5
Q310
95-th percentile15
Maximum180
Range180
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation3.954464038
Coefficient of variation (CV)0.5172524685
Kurtosis17.18284069
Mean7.645133235
Median Absolute Deviation (MAD)2.5
Skewness1.419661742
Sum4928167.56
Variance15.63778583
MonotocityNot monotonic
2021-01-05T14:31:18.985775image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5553048.4%
 
4512367.8%
 
6512227.8%
 
7433936.6%
 
8383065.8%
 
4.5350375.3%
 
10309394.7%
 
9296624.5%
 
12281234.3%
 
5.5279664.3%
 
Other values (318)25342738.7%
 
ValueCountFrequency (%) 
026920.4%
 
0.151< 0.1%
 
0.218< 0.1%
 
0.2515< 0.1%
 
0.37< 0.1%
 
ValueCountFrequency (%) 
1802< 0.1%
 
1501< 0.1%
 
1191< 0.1%
 
1151< 0.1%
 
1001< 0.1%
 

trunk_girth
Real number (ℝ≥0)

MISSING
SKEWED

Distinct427
Distinct (%)0.1%
Missing8088
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.6271860609
Minimum0
Maximum5200.65
Zeros767
Zeros (%)0.1%
Memory size5.0 MiB
2021-01-05T14:31:19.233066image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16
Q10.3
median0.51
Q30.84
95-th percentile1.4
Maximum5200.65
Range5200.65
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation6.809921039
Coefficient of variation (CV)10.85789603
Kurtosis530729.9944
Mean0.6271860609
Median Absolute Deviation (MAD)0.25
Skewness710.7624047
Sum406141.86
Variance46.37502456
MonotocityNot monotonic
2021-01-05T14:31:19.465220image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.2151342.3%
 
0.4145842.2%
 
0.3144192.2%
 
0.25141912.2%
 
0.35121021.8%
 
0.5117081.8%
 
0.45116901.8%
 
0.18113981.7%
 
0.6112801.7%
 
0.15101401.5%
 
Other values (417)52091679.5%
 
ValueCountFrequency (%) 
07670.1%
 
0.018< 0.1%
 
0.0220< 0.1%
 
0.033410.1%
 
0.04200< 0.1%
 
ValueCountFrequency (%) 
5200.651< 0.1%
 
1680.031< 0.1%
 
180.011< 0.1%
 
115.041< 0.1%
 
1051< 0.1%
 

longitude
Real number (ℝ)

Distinct654363
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.677526323
Minimum-3.837160628
Maximum-3.534624746
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:31:20.083717image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-3.837160628
5-th percentile-3.764460782
Q1-3.711183683
median-3.677249244
Q3-3.638506714
95-th percentile-3.601519735
Maximum-3.534624746
Range0.3025358821
Interquartile range (IQR)0.07267696925

Descriptive statistics

Standard deviation0.049064201
Coefficient of variation (CV)-0.01334163149
Kurtosis-0.48754484
Mean-3.677526323
Median Absolute Deviation (MAD)0.03572588582
Skewness-0.2022505044
Sum-2411170.134
Variance0.00240729582
MonotocityNot monotonic
2021-01-05T14:31:20.313869image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-3.69067384442< 0.1%
 
-3.602035777< 0.1%
 
-3.7649216956< 0.1%
 
-3.6011165655< 0.1%
 
-3.6020591945< 0.1%
 
-3.6014687465< 0.1%
 
-3.685439515< 0.1%
 
-3.6025309115< 0.1%
 
-3.6672443< 0.1%
 
-3.7151745993< 0.1%
 
Other values (654353)655564> 99.9%
 
ValueCountFrequency (%) 
-3.8371606281< 0.1%
 
-3.8371548521< 0.1%
 
-3.8371179021< 0.1%
 
-3.8371003571< 0.1%
 
-3.8370900581< 0.1%
 
ValueCountFrequency (%) 
-3.5346247461< 0.1%
 
-3.5410412711< 0.1%
 
-3.5522023211< 0.1%
 
-3.55267181< 0.1%
 
-3.5527108371< 0.1%
 

latitude
Real number (ℝ≥0)

Distinct654363
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.42032618
Minimum40.33046447
Maximum40.54245672
Zeros0
Zeros (%)0.0%
Memory size5.0 MiB
2021-01-05T14:31:20.840439image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum40.33046447
5-th percentile40.35728356
Q140.3831324
median40.41494847
Q340.45503103
95-th percentile40.49372567
Maximum40.54245672
Range0.2119922531
Interquartile range (IQR)0.07189863076

Descriptive statistics

Standard deviation0.04360022624
Coefficient of variation (CV)0.001078670816
Kurtosis-0.9394558219
Mean40.42032618
Median Absolute Deviation (MAD)0.03567175298
Skewness0.2334187047
Sum26501586.86
Variance0.001900979728
MonotocityNot monotonic
2021-01-05T14:31:21.206150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40.4387698642< 0.1%
 
40.453722747< 0.1%
 
40.398448296< 0.1%
 
40.453617585< 0.1%
 
40.453702145< 0.1%
 
40.430275895< 0.1%
 
40.453799595< 0.1%
 
40.45370465< 0.1%
 
40.474554993< 0.1%
 
40.41648343< 0.1%
 
Other values (654353)655564> 99.9%
 
ValueCountFrequency (%) 
40.330464471< 0.1%
 
40.330466041< 0.1%
 
40.330488551< 0.1%
 
40.330504031< 0.1%
 
40.330528231< 0.1%
 
ValueCountFrequency (%) 
40.542456721< 0.1%
 
40.542428951< 0.1%
 
40.542426031< 0.1%
 
40.54241041< 0.1%
 
40.542402711< 0.1%
 

Interactions

2021-01-05T14:30:21.193610image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:21.339891image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:21.435041image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:21.530948image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:21.626590image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:21.722785image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:21.821019image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:21.917429image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.012271image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.112358image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.214132image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.315955image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.413784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.511580image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.617895image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.724747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.829551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:22.935394image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2021-01-05T14:30:24.279934image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2021-01-05T14:30:37.384257image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:37.486416image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:37.582531image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:37.683388image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:37.787441image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:37.889327image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:37.989419image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:38.089945image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:38.189821image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:38.288977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:38.385817image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:38.485926image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:38.587552image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:38.688507image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2021-01-05T14:31:21.375928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-05T14:31:21.579113image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-05T14:31:21.779916image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-05T14:31:21.992856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-05T14:31:22.191200image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-05T14:30:41.162863image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:43.732581image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:48.199786image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-01-05T14:30:49.492457image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

Unnamed: 0common_nameobjectiddescriptionid_lotlotid_districtdistrictid_neighborhoodneighborhoodclassification_pathstreet_typestreetnamehousenumberdirection_codezipcodeserial_numberid_management_unitmanagement_unitid_greenareagreenareaspecie_abbreviationspecie_namesenescencephenological_phaseinterference_typestatuslabelglobalidparentcrown_diameterheighttrunk_girthlongitudelatitude
00Ciprés común1341750Arbol ARB_03-09_00000256333OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE ARMINZA 1NaNARB_03-09_000002563311236.09.Arminza, CalleNaNNaNCSECupressus sempervirensPERENNIFOLIOMNaNOPERATIVOÁrbol: ARB_03-09_0000025633{350951E6-7C45-41D6-B3DD-14CD8337136E}4598166.02.54.00.42-3.80284140.469239
11Ciprés común1341751Arbol ARB_03-09_00000256353OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE ARMINZA 3NaNARB_03-09_000002563511236.09.Arminza, CalleNaNNaNCSECupressus sempervirensPERENNIFOLIOMNO_ALTA_TENSIONOPERATIVOÁrbol: ARB_03-09_0000025635{70E0BD2A-E981-43EA-AE79-08ABA89240EE}4598167.02.07.00.60-3.80271440.469676
22Morera blanca1341752Arbol ARB_03-09_00000256483OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE AZPEITIA 4NaNARB_03-09_00000256483929.09.Azpeitia, CalleNaNNaNMALMorus albaCADUCIFOLIOVNaNOPERATIVOÁrbol: ARB_03-09_0000025648{C5029AE0-99A0-47C8-BDD8-7C9DCB6B3318}4598168.06.59.50.90-3.81333740.470171
33Ciprés de Arizona1341753Arbol ARB_03-09_00000252033OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE LAMIACO 4NaNARB_03-09_00000252034177.09.Lamiaco, CalleNaNNaNCARCupressus arizonicaPERENNIFOLIOMNaNOPERATIVOÁrbol: ARB_03-09_0000025203{B6726857-D54C-48E8-B2E5-D06436551814}4598169.02.04.00.80-3.81951140.473073
44Ciprés de Arizona1341754Arbol ARB_03-09_00000252053OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE LAMIACO 4NaNARB_03-09_00000252054177.09.Lamiaco, CalleNaNNaNCARCupressus arizonicaPERENNIFOLIOMNaNOPERATIVOÁrbol: ARB_03-09_0000025205{90108F95-7B9C-4A4B-B5D9-9AF528196C68}4598170.02.04.00.52-3.81932640.473022
55Ciprés de Arizona1341755Arbol ARB_03-09_00000252433OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE GOBELAS 28NaNARB_03-09_00000252433278.09.Gobelas, CalleNaNNaNCARCupressus arizonicaPERENNIFOLIOMNaNOPERATIVOÁrbol: ARB_03-09_0000025243{675F450D-CB45-40BA-BDF2-DAAA3B18E71D}4598171.01.53.50.47-3.82350140.472980
66Plátano de sombra1341756Arbol ARB_03-09_00000253003OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE MOTRICO 8NaNARB_03-09_00000253005137.09.Motrico, CalleNaNNaNPDAPlatanus x hybridaCADUCIFOLIOMNO_ALTA_TENSIONOPERATIVOÁrbol: ARB_03-09_0000025300{C366E459-E3E7-432B-8934-504FBF7F8D0B}4598172.06.016.00.80-3.81231240.469508
77Plátano de sombra1341757Arbol ARB_03-09_00000253023OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE MOTRICO 8NaNARB_03-09_00000253025137.09.Motrico, CalleNaNNaNPDAPlatanus x hybridaCADUCIFOLIOMNO_ALTA_TENSIONOPERATIVOÁrbol: ARB_03-09_0000025302{782D203C-94F7-4A11-9EC5-D0CE0FAEF0D1}4598173.07.018.00.85-3.81225140.469527
88Pino piñonero1341758Arbol ARB_03-09_00000253533OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE CANTOS NEGROS 3NaNARB_03-09_00000253531454.09.Cantos Negros, CalleNaNNaNPPIPinus pineaPERENNIFOLIOMNaNOPERATIVOÁrbol: ARB_03-09_0000025353{0DC9D545-7A70-4AA9-B141-B33F4859D6D7}4598174.04.011.00.95-3.83240640.475828
99Pino piñonero1341759Arbol ARB_03-09_00000253683OESTE9.0MONCLOA-ARAVACA96.0EL PLANTÍOJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNCALLE CANTOS NEGROS 10NaNARB_03-09_00000253681454.09.Cantos Negros, CalleNaNNaNPPIPinus pineaPERENNIFOLIOVNO_ALTA_TENSIONOPERATIVOÁrbol: ARB_03-09_0000025368{1E1F1282-2C17-4CEF-865A-026B460EF5E0}4598175.07.016.01.42-3.83383540.476243

Last rows

Unnamed: 0common_nameobjectiddescriptionid_lotlotid_districtdistrictid_neighborhoodneighborhoodclassification_pathstreet_typestreetnamehousenumberdirection_codezipcodeserial_numberid_management_unitmanagement_unitid_greenareagreenareaspecie_abbreviationspecie_namesenescencephenological_phaseinterference_typestatuslabelglobalidparentcrown_diameterheighttrunk_girthlongitudelatitude
655640655640NaN1997540Arbol plantado3PARQUES FORESTALES3.0RETIRO35.0LOS JERÓNIMOSJARDINERIA \ ARBOLES \ ARBOLCALLEPuerta del Angel Caido4Arbol plantado cerca de la estufa 2028009NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO PREPARADOÁrbol:{A393EF62-0131-4677-96E7-39BB8EC99231}NaNNaNNaNNaN-3.68536440.409533
655641655641Taray1997541NaN1PARQUES HISTÓRICOS3.0RETIRO35.0LOS JERÓNIMOSJARDINERIA \ ARBOLES \ ARBOLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTAXTamarix spCADUCIFOLIONaNNaNACTIVOÁrbol:{34F00624-A2B4-4FCF-BC7D-CDB75784B4B3}NaNNaNNaNNaN-3.68761740.410718
655642655642Almez1997542Arbol3PARQUES FORESTALES3.0RETIRO35.0LOS JERÓNIMOSJARDINERIA \ ARBOLES \ ARBOLCALLEPuerta del Angel Caido4Arbol cercano a la estufa 2228009NaNNaNNaNNaNNaNCAUCeltis australisCADUCIFOLIOMOTROSNO PREPARADOÁrbol:{358A44ED-5C5E-4FC6-919E-358D4294CAA2}NaN8.08.51.5-3.68570540.409201
655643655643Taray1997543NaN1CENTRO-OESTE1.0CENTRO14.0JUSTICIAJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNNaNNaNNaN3327.01.Gran Vía, CalleNaNNaNTAXTamarix spCADUCIFOLIONaNNaNNO PREPARADOÁrbol:{8DD5FC80-4B88-4525-B37E-003C466A4ACB}4736879.0NaNNaNNaN-3.70007140.419945
655644655644Taray1997544aa1CENTRO-OESTE1.0CENTRO16.0SOLJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNNaNNaNNaN4846.01.Mayor, CalleNaNNaNTAXTamarix spCADUCIFOLIONaNNaNNO PREPARADOÁrbol:{25471FE5-8233-424D-9B47-2C3E42427FF0}4736879.0NaNNaNNaN-3.70649340.416332
655645655645Taray1997545NaN1CENTRO-OESTE1.0CENTRO16.0SOLJARDINERIA \ ARBOLES \ ARBOL_VIARIONaNNaNNaNNaNNaNNaN4846.01.Mayor, CalleNaNNaNTAXTamarix spCADUCIFOLIONaNNaNNO PREPARADOÁrbol:{BEF77DC5-A314-4EB9-A594-0D29094317E9}4736879.0NaNNaNNaN-3.70533540.416518
655646655646NaN1997550Arbol3PARQUES FORESTALES9.0MONCLOA-ARAVACA91.0CASA DE CAMPOJARDINERIA \ ARBOLES \ ARBOLCALLECalle Rosaleda2Cedro en la pradera28008NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO PREPARADOÁrbol:{765A9B8A-A33D-4565-86EF-F4F063A87614}NaNNaNNaNNaN-3.72156040.427335
655647655647Cedro llorón1997551Arbol3PARQUES FORESTALES9.0MONCLOA-ARAVACA91.0CASA DE CAMPOJARDINERIA \ ARBOLES \ ARBOLCALLECalle Rosaleda2Cedro en la pradera28008NaNNaNNaNNaNNaNCDECedrus deodaraPERENNIFOLIOVNaNNO PREPARADOÁrbol:{23F65D09-FF9A-4F42-83A2-82B98E8881ED}NaN12.023.03.7-3.72156040.427335
655648655648NaN1997552Arbol -test-1CENTRO-OESTE1.0CENTRO14.0JUSTICIANaNNaNNaNNaNNaNNaNNaNNaNNaN116943.0PZA. DE CIBELES-FUENTE E ISLETASNaNNaNNaNNaNNaNNO PREPARADOÁrbol:{577ED854-DCB7-467A-880D-8013B864E0C8}NaNNaNNaNNaN-3.69310740.419355
655649655649Almez1997555Arbol3PARQUES FORESTALES3.0RETIRO35.0LOS JERÓNIMOSJARDINERIA \ ARBOLES \ ARBOLCALLEPuerta del Angel Caido4Arbol cercano a la estufa 2228009NaNNaNNaNNaNNaNCAUCeltis australisCADUCIFOLIOMOTROSNO PREPARADOÁrbol:{E5DD6387-5E0F-4958-9269-123103E3A1D3}NaN8.08.51.5-3.68570540.409201